Geometry vs growth: Internal consistency of the flat {\Lambda}CDM model with KiDS-1000
J. Ruiz-Zapatero, Benjamin St\"olzner, Benjamin Joachimi, Marika, Asgari, Maciej Bilicki, Andrej Dvornik, Benjamin Giblin, Catherine Heymans,, Hendrik Hildebrandt, Arun Kannawadi, Konrad Kuijken, TilmanTr\"oster, Jan, Luca van den Busch, Angus H. Wright

TL;DR
This study tests the internal consistency of the flat Lambda Cold Dark Matter model using multiple cosmological probes, finding persistent tensions in matter fluctuation amplitude and hints of higher Hubble constant values, with results consistent across geometry and growth parameters.
Contribution
It introduces a split of the LambdaCDM model into geometry and growth components, independently constrained by diverse observational data, to investigate internal model consistency and potential tensions.
Findings
Strong consistency between geometry and growth parameters.
Persistent tension in the $S_8$ parameter at about 3σ.
Hints of higher $H_0$ and lower $ m{ extbf{ extit{ extOmega}}}_{ m{m}}$ values compared to Planck.
Abstract
We carry out a multi-probe self-consistency test of the flat CDM model with the aim of exploring potential causes of the reported tensions between high- and low-redshift cosmological observations. We divide the model into two theory regimes determined by the smooth background (geometry) and the evolution of matter density fluctuations (growth), each governed by an independent set of Lambda Cold Dark Matter (CDM) cosmological parameters. This extended model is constrained by a combination of weak gravitational lensing measurements from the Kilo-Degree Survey, galaxy clustering signatures extracted from Sloan Digital Sky Survey campaigns and the Six-Degree Field Galaxy Survey, and the angular baryon acoustic scale and the primordial scalar fluctuation power spectrum measured in cosmic microwave background (CMB) data. We find strong consistency between…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
